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on Econometric Time Series |
| By: | Jesus Gonzalo; Jean-Yves Pitarakis |
| Abstract: | In this paper we introduce threshold type nonlinearities within a single equation cointegrating regression model and propose a testing procedure for testing the null hypothesis of linear cointegration versus cointegration with threshold effects. Our framework allows the modelling of long run equilibrium relationships that may switch according to the magnitude of a threshold variable assumed to be stationary and ergodic and thus constitutes an attempt to deal econometrically with the potential presence of multiple equilibria. The framework is flexible enough to accomodate regressor endogeneity and serial correlation. |
| Date: | 2006–06 |
| URL: | http://d.repec.org/n?u=RePEc:cte:werepe:we20060621&r=ets |
| By: | Helena Veiga |
| Abstract: | According to the Taylor-Effect the autocorrelations of absolute financial returns are higher than the ones of squared returns. In this work, we analyze this empirical property for three different asymmetric stochastic volatility models, with short and/or long memory. Specially, we investigate how the Taylor-Effect relates to the most important model characteristics: its asymmetry and its capacity to generate volatility persistence and kurtosis. Finally, we realize Monte Carlo experiments to infer about possible biases of the sample Taylor-Effect and fit the models to the return series of the Dow Jones. |
| Date: | 2007–02 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws070702&r=ets |
| By: | Todd Prono |
| Abstract: | This paper presents a new method for identifying triangular systems of time-series data. Identification is the product of a bivariate GARCH process. Relative to the literature on GARCH-based identification, this method distinguishes itself both by allowing for a timevarying covariance and by not requiring a complete estimation of the GARCH parameters. Estimation follows OLS and standard univariate GARCH and ARMA techniques, or GMM. A Monte Carlo study of the GMM estimator is provided. The identification method is then applied in testing a conditional version of the CAPM. |
| Keywords: | Capital assets pricing model ; Time-series analysis |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedbwp:07-1&r=ets |
| By: | Westerlund, Joakim (Department of Economics, Lund University) |
| Abstract: | One of the most cited studies in recent years within the field of nonstationary panel data analysis is that of Bai and Ng (2004, A PANIC Attack on Unit Roots and Cointegration. Econometrica 72, 1127-1177), in which the authors propose PANIC, a new framework for analyzing the nonstationarity of panels with idiosyncratic and common components. This paper shows that, although valid at the level of the individual unit, PANIC is not an asymptotically valid framework for pooling tests at the aggregate panel level. |
| Keywords: | Panel Unit Root Test; Pooling; Common Factor; Cross-Sectional Dependence |
| JEL: | C21 C22 C23 |
| Date: | 2007–02–19 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:lunewp:2007_005&r=ets |
| By: | Christos S. Savva; Denise R. Osborn; Len Gill |
| Abstract: | This study extends the dynamic conditional correlation model to allow day-specific correlations of shocks across international stock markets. The properties of the resulting periodic dynamic conditional correlation (PDCC) model are examined, with the model then applied to study the intra-week interactions between six developed European stock markets and the US over the period 1993 - 2005. We find very strong evidence of periodic effects in the conditional correlations of the shocks. The highest correlations are generally observed on Thursdays, with these Thursday correlations in some cases being twice those on Monday or Tuesday. Prior to estimating the PDCC model, periodic mean and volatility effects are removed using a PAR model for returns combined with a periodic EGARCH specification for the variance equation. Strong periodic mean effects are found for returns in the French, Italian and Spanish stock markets, whereas such effects are present in volatility for all stock markets except Italy. |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:man:cgbcrp:77&r=ets |
| By: | Silja Kinnebrock; Mark Podolskij |
| Abstract: | In this paper we present the central limit theorem for general functions of the increments of Brownian semimartingales. This provides a natural extension of the results derived in Barndorff-Nielsen, Graversen, Jacod, Podolskij & Shephard (2006), who showed the central limit theorem for even functions. We prove an infeasible central limit theorem for general functions and state some assumptions under which a feasible version of our results can be obtained. Finally, we present some examples from the literature to which our theory can be applied. |
| Keywords: | Bipower Variation; Central Limit Theorem; Diffusion Models; High-Frequency Data; Semimartingale Theory |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2007fe03&r=ets |
| By: | Federico Ravenna (University of California) |
| Abstract: | Dynamic Stochastic General Equilibrium models are often tested against empirical VARs or estimated by minimizing the distance between the model's and the VAR impulse response functions. These methodologies require that the data-generating process consistent with the DSGE theoretical model has a VAR representation. This paper discusses the assumptions needed for a finite-order VAR(p) representation of any subset of a DSGE model variables to exist. When a VAR(p) is only an approximation to the true VAR, the paper shows that the truncated VAR(p) may return largely incorrect estimates of the impulse response function. The results do not hinge on an incorrect identification strategy or on small sample bias. But the bias introduced by truncation can lead to bias in the identification of the structural shocks. Identification strategies that are equivalent in the true VAR representation perform differently in the approximating VAR. |
| Keywords: | vector autoregression, dynamic stochastic general equilibrium model, business cycle shocks |
| JEL: | C13 C22 E32 |
| Date: | 2006–08 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:0619&r=ets |